How Does ChatGPT Describe Your Brand? (And Why It Probably Differs From What You'd Say)

Most brands have no idea what AI is saying about them.

Not because they haven't checked — but because checking isn't straightforward. You can't just ask ChatGPT "what do you think of my brand?" and get a reliable answer. AI models are inconsistent, context-dependent, and trained on data that may be months or years old.

Here's how to actually find out what AI is saying about your brand — and what to do when the answer isn't what you expected.

Why AI Brand Description Matters More Than You Think

In traditional search, your brand controls its narrative through its website, press releases, and SEO content. In AI search, the narrative is reconstructed from thousands of third-party sources — reviews, forum posts, comparison articles, news coverage.

The AI's description of your brand is a weighted average of everything people have written about you online. Your own content is a small fraction of that signal.

This creates two problems:

Outdated positioning. If your brand pivoted 18 months ago, AI may still describe the old version. Training data takes time to update. Brands that repositioned recently often find AI confidently describing their former identity.

Inconsistent narrative across AI engines. ChatGPT might describe your brand accurately. Claude might get your category right but miss your key differentiator. Kimi might default to a generic description that fits 10 competitors equally well.

How to Find Out What AI Actually Says

Method 1: Manual sampling (fast, imprecise)

Ask 3–4 AI models the same questions about your brand:

• "What is [Brand]?"
• "What is [Brand] best known for?"
• "Who should use [Brand]?"
• "How does [Brand] compare to [Competitor]?"

Record the answers. Look for: consistency, accuracy, what's missing, what's wrong.

Limitation: This only tests the branded queries where you'd naturally appear. The more important question — "does AI recommend you when users don't know to ask?" — isn't answered by this method.

Method 2: Scenario-based visibility scan (complete picture)

Run your brand through 10–12 query scenarios covering recommendation queries, comparison queries, trust queries, and category queries. This is what AI visibility tools like Anchor automate.

The result tells you not just what AI says when asked about you directly, but whether AI mentions you in the scenarios where your customers are making decisions.

What to Look For in AI Brand Descriptions

Accuracy — Is the core description factually correct? Brand founding, core product, category, key differentiators.

Recency — Does the description reflect your current positioning? If you launched a major product or rebranded, AI may lag by 6–18 months.

Sentiment — Positive, neutral, or subtly negative? "Brand X is a reasonable option" vs. "Brand X is a leading choice" are both positive, but one is damning with faint praise.

Completeness — Does AI describe your key differentiator, or does it describe you the same way it would describe 5 competitors? Generic descriptions ("a project management tool for teams") signal poor category differentiation in AI training data.

Cross-engine consistency — Does ChatGPT say the same things as Claude? As Kimi? Inconsistency usually traces back to uneven content coverage across the platforms each model weights differently.

Common Problems and Their Fixes

Problem: AI describes you by what you are, not what you're best for

"[Brand] is a marketing automation platform" — factually accurate, completely unhelpful for positioning.

Fix: Create content that explicitly maps your brand to specific use cases and user types. "Best [Brand] use cases for [audience]" articles get cited by AI at much higher rates than general brand descriptions.

Problem: AI mentions your competitors in the same breath, ranking them above you

Fix: Publish structured comparison content where your brand clearly wins on specific dimensions. AI tends to reproduce the rankings it finds in comparison articles — so the absence of favorable comparison content is self-defeating.

Problem: Chinese AI (Kimi, DeepSeek) describes you differently than English AI

Fix: This almost always means your Chinese-language content coverage is thin. A brand with extensive English coverage but minimal Zhihu/Xiaohongshu presence will see this exact gap. Chinese AI engines weight Chinese-language sources heavily.

Problem: AI mentions a controversy or negative event prominently

Fix: This is the hardest one. AI models reflect the balance of coverage. A highly-covered negative event will stay in AI descriptions until it's buried by substantial positive coverage volume. Proactive content creation across multiple platforms over 6+ months is typically required.

The Benchmark Question

Don't just ask what AI says about you. Ask what AI says about your top competitor — then compare.

If your competitor's AI description is more specific, more positive, and more focused on purchase decision scenarios than yours, that gap is directly costing you in AI-driven recommendations. The fix is content strategy, not brand strategy.

Check your brand's AI description at anchor.agentese.ai — the report shows exactly what each major AI says about you, where the narrative diverges, and what content would close the gap.

Anchor is an AI brand visibility scanner that measures how accurately and consistently AI models describe your brand across ChatGPT, Claude, Gemini, Kimi, and DeepSeek.